Strategies for running the QAOA at hundreds of qubits

Understand

We explore strategies aimed at reducing the amount of computation, both quantum and classical, required to run the Quantum Approximate Optimization Algorithm (QAOA).

• First, following Wurtz et al.
• [Phys.Rev A 104:052419], we consider the standard QAOA with instance-independent "tree" parameters chosen in advance.
• These tree parameters are chosen to optimize the MaxCut expectation for large girth graphs.